Consumer health information is growing in importance and popularity, with computer networks such as the Internet providing a growing share of the information. It is estimated that health issues are addressed at tens of thousands of online sites with potentially millions of pages of health or benefit-related works. With a general lack of clinical and editorial standards for health or benefit-related works, lay consumers without specific medical training, and even trained medical professionals, can have relatively little success in finding desired or relevant information among such vast resources.
Moreover, given the extremely personal nature of health, most individuals have minimal interest in browsing materials that have no relevance to their health or the health of their families. Yet most of the health information available at conventional network (e.g., Internet) sites or portals addresses only general topics. Such information seldom has any particular relevance to individual users. Accordingly, there is a need for an improved way of obtaining relevant or personalized health or benefit-related works from computer networks such as the Internet.
Conventional network (e.g., Internet) systems employ a variety of personalization processes that at least minimally personalize a network site for different visitors or users. The personalization provided by many such processes is relatively simplistic and provides personalization only to the extent of a small number of personalization options. These conventional personalization processes include Greetings, which can be as simple as providing a “welcome sign” that informs the user of the state of a single condition, such as, “Hello you've got mail;” Pick Lists, which allow users to select from predetermined lists of news categories, horoscopes, sports scores, etc.; Keywords, codes or symbols, which can be referenced by entering keywords such as zip codes for local weather forecasts or stock ticker symbols for stock quotes; Demographic/traffic analysis, which is usually derived from a log file which indicates a user's name, email address, zip code, and Internet Service Provider information; Comparison methods, which use data provided by other users to highlight similarities and differences among users; and Collaborative processes, which select works based on the preferences of others who are in some way similar to the user.
Personalization processes in use today, including the use of demographics and pick-lists, are inadequate for the vast amounts of health or benefit-related information and the relatively narrow interests of many users. Pick Lists are useful, when the possible selections number fewer than several (e.g., 4 or 5) dozens. However, health related works can be usefully categorized among hundreds or thousands of distinct topics. As a consequence, conventional health-related network sites that employ Pick Lists for personalization typically provide relatively few selections that each cover broad areas of information. Such broad coverage areas render such personalization ineffective for the specific health or benefit-related information desired by many users.